INTRODUCTION TO NEURAL NETWORKS FOR C# PDF

adminComment(0)

Introduction to Neural Networks for C#, Second Edition IX This book is dedicated to my neurons, without whose constant support this book would not have been. This book introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward. Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural.


Introduction To Neural Networks For C# Pdf

Author:ESTEFANA LIRIANO
Language:English, German, French
Country:Iran
Genre:Fiction & Literature
Pages:266
Published (Last):15.02.2016
ISBN:825-9-23050-258-7
ePub File Size:16.53 MB
PDF File Size:19.76 MB
Distribution:Free* [*Register to download]
Downloads:24515
Uploaded by: JONNA

Programming Neural Networks with Encog 3 in C# , PDF .. networks, reference “Introduction to Neural Networks with Java” and “Intro-. Apr 17, Introduction to Neural Networks for C#, 2nd Edition by Jeff Heaton Introduction to Neural Networks for C#, 2nd Edition pdf Introduction to Neural. Introduction. Welcome to the “An introduction to neural networks for beginners” book. This book consists of Part A of a much larger, forthcoming book – “From.

Chapter 6 expands upon backpropagation by showing how to train a network using a genetic algorithm. A genetic algorithm creates a population of neural networks and only allows the best networks to?

Simulated annealing can also be a very effective means of training a feedforward neural network. Chapter 7 continues the discussion of training methods by introducing simulated annealing. Simulated annealing simulates the heating and cooling of a metal to produce an optimal solution. Neural networks may contain unnecessary neurons.

Chapter 8 explains how to prune a neural network to its optimal size. Pruning allows unnecessary neurons to be removed from the neural network without adversely affecting the error rate of the network. The neural network will process information more quickly with fewer neurons. Prediction is another popular use for neural networks. Chapter 9 introduces temporal neural networks, which attempt to predict the future.

Prediction networks can be applied to many different problems, such as the prediction of sunspot cycles, weather, and the financial markets. Chapter 10 builds upon chapter 9 by demonstrating how to apply temporal neural networks to the financial markets. Another neural network architecture is the self-organizing map SOM. An SOM uses a winner-takes-all strategy, in which the output is provided by the winning neuron?

Créez un blog gratuitement et facilement sur free!

Chapter 11 provides an introduction to SOMs and demonstrates how to use them. Handwriting recognition is a popular use for SOMs. Chapter 12 continues where chapter 11 leaves off, by demonstrating how to use an SOM to read handwritten characters. The neural network must be provided with a sample of the handwriting that it is to analyze.

This handwriting is categorized using the 26 characters of the Latin alphabet. The neural network is then able to recognize new characters. Chapter 13 introduces bot programming and explains how to use a neural network to help identify data. Bots are computer programs that perform repetitive tasks.

An HTTP bot is a special type of bot that uses the web much like a human uses it. The neural network is trained to recognize the specific types of data for which the bot is searching.

The Encog neural network framework is also introduced. In Figure 1-b, "male" is encoded as In addition to encoding non-numeric x-data, in many problems numeric x-data is normalized so that the magnitudes of the values are all roughly in the same range. In Figure 1-b, the age value of 35 is normalized to 3. The idea is that without normalization, x-variables that have values with very large magnitudes can dominate x-variables that have values with small magnitudes.

The heart of a neural network is represented by the central box. A typical neural network has three levels of nodes. The input nodes hold the x-values. The hidden nodes and output nodes perform processing. In Figure 1-b, the output values are 0. These three values loosely represent the probability of conservative, liberal, and moderate respectively. Because the y-value associated with moderate is the highest, the neural network concludes that the year-old male has a political inclination that is moderate.

The number of input and output nodes are determined by the structure of the problem data. But the number of hidden nodes can vary and is typically found through trial and error.

Programming Neural Networks with Encog3 in C#, 2nd Edition

Each of these lines represents a numeric value, for example Also, each hidden and output node but not the input nodes has an additional special kind of weight, shown as a red line in the diagram. These special weights are called biases. A neural network's output values are determined by the values of the inputs and the values of the weights and biases.

So, the real question when using a neural network to make predictions is how to determine the values of the weights and biases. This process is called training. Put another way, training a neural network involves finding a set of values for the weights and biases so that when presented with training data, the computed outputs closely match the known, desired output values.

Once the network has been trained, new data with unknown y- values can be presented and a prediction can be made. This book will show you how to create neural network systems from scratch using the C programming language.

Related titles

There are existing neural network applications you can use, so why bother creating your own? There are at least four reasons. First, creating your own neural network gives you complete control over the system and allows you to customize the system to meet specific problems. Second, if you learn how to create a neural network from scratch, you gain a full understanding of how neural networks work, which allows you to use existing neural network applications more effectively.

Third, many of the programming techniques you learn when creating neural networks can be used in other programming scenarios. And fourth, you might just find creating neural networks interesting and entertaining. Data Encoding and Normalization One of the essential keys to working with neural networks is understanding data encoding and normalization.

Take a look at the screenshot of a demo program in Figure 1-c.

The demo program begins by setting up four hypothetical training data items with x-values for people's gender, age, home location, and annual income, and y-values for political inclination conservative, liberal, or moderate. The first line of dummy data is: Male 25 Rural 63, There are two kinds of encoding used, effects encoding for non-numeric x-values and dummy encoding for non-numeric y-values. The first line of the resulting encoded data is: -1 25 1 0 63, Next, the demo performs normalization on the numeric x-values age and income.An SOM uses a winner-takes-all strategy, in which the output is provided by the winning neuron?

Citations per year

The demo program uses two different types of normalization just to illustrate the two techniques. An SOM uses a winner-takes-all strategy, in which the output is provided by the winning neuron? Let's say we have an error in one of the cells in the output layer.

You will be shown how to construct a Hopfield neural network and how to train it to recognize patterns.

Figure 1-b: A Neural Network As you will see shortly, a neural network is essentially a complicated mathematical function that understands only numbers. The demo program uses a derivation of the definition to avoid arithmetic overflow. Handwriting recognition is a popular use for SOMs. So, the real question when using a neural network to make predictions is how to determine the values of the weights and biases.